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Enhancing Deep Reinforcement Learning with Compressed Sensing-based State Estimation
https://ipsj.ixsq.nii.ac.jp/records/227103
https://ipsj.ixsq.nii.ac.jp/records/227103901040f9-c5be-4ac3-b00c-d6b221b0cb50
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-ARC23254026.pdf (3.1 MB)
2025年7月27日からダウンロード可能です。
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Copyright (c) 2023 by the Information Processing Society of Japan
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| 非会員：¥660, IPSJ:学会員：¥330, ARC:会員：¥0, DLIB:会員：¥0 | ||
| Item type | SIG Technical Reports(1) | |||||||||
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| 公開日 | 2023-07-27 | |||||||||
| タイトル | ||||||||||
| タイトル | Enhancing Deep Reinforcement Learning with Compressed Sensing-based State Estimation | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Enhancing Deep Reinforcement Learning with Compressed Sensing-based State Estimation | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | 機械学習 | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||||
| 資源タイプ | technical report | |||||||||
| 著者所属 | ||||||||||
| Keio University | ||||||||||
| 著者所属 | ||||||||||
| Keio University／RIKEN | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Keio University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Keio University / RIKEN | ||||||||||
| 著者名 |
Shaswot, Shresthamali
× Shaswot, Shresthamali
× Masaaki, Kondo
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| 著者名(英) |
Shaswot, Shresthamali
× Shaswot, Shresthamali
× Masaaki, Kondo
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | In various real-world applications, sensor data collected for adaptive control using Reinforcement Learning (RL) often suffer from missing information due to sensor failures, data transmission errors, or other sources of noise. Such missing data can significantly hinder the agent's ability to make informed decisions. In this paper, we propose a novel approach to address this challenge by leveraging Compressed Sensing (CS) techniques to recover missing information from the sensor data and reconstruct the state observation. The reconstructed state is then fed to the RL agents. As a result, they exhibit enhanced robustness and intelligence, surpassing the performance achievable when solely presented with noisy data as state input. | |||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | In various real-world applications, sensor data collected for adaptive control using Reinforcement Learning (RL) often suffer from missing information due to sensor failures, data transmission errors, or other sources of noise. Such missing data can significantly hinder the agent's ability to make informed decisions. In this paper, we propose a novel approach to address this challenge by leveraging Compressed Sensing (CS) techniques to recover missing information from the sensor data and reconstruct the state observation. The reconstructed state is then fed to the RL agents. As a result, they exhibit enhanced robustness and intelligence, surpassing the performance achievable when solely presented with noisy data as state input. | |||||||||
| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN10096105 | |||||||||
| 書誌情報 |
研究報告システム・アーキテクチャ（ARC） 巻 2023-ARC-254, 号 26, p. 1-11, 発行日 2023-07-27 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 2188-8574 | |||||||||
| Notice | ||||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||||
| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
